J. Qin (Gwen)

Gwen has been a member of the Pervasive Systems group within the Faculty of EEMCS at the University of Twente since 2023. She obtained her master’s degree in Computer Science from the University of Tsukuba in 2023.

Her current research centers on collaborative Augmented Reality and Extended Reality experiences, with applications in predictive maintenance and remote collaboration. She specifically investigates challenges such as spatial inconsistency and latency in multi-user XR environments, while also addressing user experience and accessibility aspects, including cybersickness, color vision deficiency, and depth perception. Additionally, she explores how LLMs can enhance context-awareness and interaction in XR systems.

Her areas of expertise include Extended Reality, Metaverse, Computer Vision, Image Processing, 3D Modeling, and Machine Learning.

Organisations

Are you a bachelor’s or master’s student curious about Extended Reality (XR), Augmented Reality (AR), or Virtual Reality (VR)? Here’s an exciting opportunity to work on enhancing AR experiences for collaborative environments! If you're interested in XR and eager to make a significant impact on user experience in cutting-edge collaborative settings, this project is for you.

I have several assignments related to augmented reality that you can explore [here]. If you’re interested in these assignments or have your own research ideas that align with my interests, I’d be happy to discuss them with you. Please feel free to reach out.

My research interests also lie in 3D Modeling, Computer Vision, and Image Processing, which do not necessarily need to be connected to XR/VR/AR.

Below are some thesis assignments I have supervised.

Supervision of students' projects:

• Exploration of Different Large Language Models for Retrieval-Augmented Generation in Analyzing Wearable Running Data for Sports Physiotherapy
• Network Simulation for Investigating Latency Contributors in Collaborative AR Systems
• Personalized Color Vision Deficiency Detection and Enhancement Pipeline in Consumer AR
• Beyond Vision: The Sound of Motion in Virtual Reality
• Enhancing Depth Perception in Virtual Reality for Individuals with Unilateral Amblyopia using Monocular Visual Cues

PrimaVera 

Enhances reliability with scalable predictive maintenance and optimization

No more train delays, power outages, or failure of production machines? The PrimaVera project, funded by the Dutch National Research Agenda (NWA), represents a major step towards this goal. With predictive maintenance, or just-in-time maintenance (maintenance just before a system breaks down), the reliability of infrastructure and production resources can be increased and the costs of maintenance can be reduced.

Existing predictive maintenance techniques only work for small-scale systems and are difficult to scale up. Choices made in one place in the chain have an important influence on other processes in the chain. The choice of a certain type of sensors and measurements influences the type of predictions that can be made, and therefore also the quality of the predictions. That is why cross-level optimization methods are being developed within PrimaVera.

Sublime

Large steel infrastructures, like bridges, tunnels and sheet piles for water ingress protection are essential for modern society. Many of these existing structures are subject to degradation, resulting in structural safety issues. This has a negative impact on our comfort and well-being due to unforeseen (partial) closing of structures and large associated investments. Many developed countries are struggling with this issue and it is urgent in The Netherlands because of the large number of heavy vehicles, methods of construction in previous times and brackish water conditions. Consequently, the Dutch Ministry of Infrastructure and Water Management is now budgeting large investments for renovation and replacement requiring 3 to 5 billion euros annually and causing large disruption due to downtime. In addition to these economic implications, steel production requires a vast amount of raw material and energy. This conflicts with the Dutch aim of achieving a sustainable economy and meeting the Paris climate agreements. Central question is, how can we increase availability, reduce costs and contribute to enhanced circularity for these structures?

Address

University of Twente

Zilverling (building no. 11), room 5007
Hallenweg 19
7522 NH Enschede
Netherlands

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Organisations